Driving change: Electric vehicle charging behavior and peak loading

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dc.contributor.authorWilliams B
dc.contributor.authorBishop, Daniel
dc.contributor.authorHooper G
dc.contributor.authorChase, Geoff
dc.date.accessioned2024-01-08T01:33:25Z
dc.date.available2024-01-08T01:33:25Z
dc.date.issued2024
dc.description.abstractElectric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.
dc.identifier.citationWilliams B, Bishop D, Hooper G, Chase JG (2024). Driving change: Electric vehicle charging behavior and peak loading. Renewable and Sustainable Energy Reviews. 189. 113953-113953.
dc.identifier.doihttp://doi.org/10.1016/j.rser.2023.113953
dc.identifier.issn1364-0321
dc.identifier.issn1879-0690
dc.identifier.urihttps://hdl.handle.net/10092/106492
dc.languageen
dc.publisherElsevier BV
dc.rights© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttp://hdl.handle.net/10092/17651
dc.subjectelectric vehicle
dc.subjectcharging demand
dc.subjectagent-based modeling
dc.subjectuser behaviour
dc.subjectMonte Carlo modeling
dc.subjectelectricity demand
dc.subject.anzsrc40 - Engineering::4008 - Electrical engineering::400805 - Electrical energy transmission, networks and systems
dc.subject.anzsrc40 - Engineering::4002 - Automotive engineering::400205 - Hybrid and electric vehicles and powertrains
dc.subject.anzsrc40 - Engineering::4005 - Civil engineering::400512 - Transport engineering
dc.subject.anzsrc40 - Engineering::4005 - Civil engineering::400508 - Infrastructure engineering and asset management
dc.titleDriving change: Electric vehicle charging behavior and peak loading
dc.typeJournal Article
uc.collegeFaculty of Engineering
uc.departmentMechanical Engineering
uc.departmentCivil and Natural Resources Engineering
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